Senior Data Operations Engineer – Scale Reliable Pipelines

AVN DIGITAL PTE. LTD.; Hong Kong; On-site; HKD 550,000 - 900,000; Full time

Benefits offered by this job

Competitive salary and benefits

Job summary

Avensys is a leading IT professional services company seeking a Senior Data Operations Engineer to support, monitor, troubleshoot, and optimize enterprise data platforms and pipelines across batch and real-time processing environments.

The role focuses on reliability, data quality, and performance, requiring collaboration with Data Engineers, cloud/infrastructure, and business teams to resolve production issues and implement permanent improvements. Occasional on-call support may be required.

Qualifications

• Strong hands-on experience with SQL and Python.
• Experience supporting ETL/ELT pipelines and data integration workflows.
• Experience with enterprise-scale data platforms in production environments.
• Familiarity with data lakes, data warehouses and lakehouse architectures.

Responsibilities

• Monitor and manage ETL/ELT pipelines and data workflows in production.
• Ensure SLA, availability, and performance of data pipelines.
• Troubleshoot pipeline and data quality issues; perform RCA and implement solutions.
• Collaborate with data engineers, cloud/infrastructure, applications, and business stakeholders.
• Develop automation scripts using Python, SQL, or shell scripting.

Skills

Data Operations

SQL & Python

ETL/ELT pipelines

Root cause analysis

Incident management

Tools

Apache Spark

Apache Airflow

Azure Data Factory

Databricks

Snowflake

Azure Synapse

BigQuery

Linux/Shell

Git / CI/CD

Jenkins / Azure DevOps

Splunk / Grafana / Prometheus

Cloud platforms (AWS/Azure/GCP)

Job description

Avensys is a leading IT professional services company seeking a Senior Data Operations Engineer to support, monitor, troubleshoot, and optimize enterprise data platforms and pipelines across batch and real-time processing environments.

The role focuses on reliability, data quality, and performance, requiring collaboration with Data Engineers, cloud/infrastructure, and business teams to resolve production issues and implement permanent improvements. Occasional on-call support may be required.